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Rural Long-Term Care and Informal Caregiving

  • Nan E. Johnson
Chapter
Part of the Understanding Population Trends and Processes book series (UPTA, volume 7)

Abstract

This chapter uses the 2004 National Nursing Home Survey (NNHS) to update an earlier study by Duncan and Radcliff (Nursing homes and community-based long-term care. In: Glasgow N, Morton LW, Johnson NE (eds) Critical issues in rural health, Blackwell Publishing, Ames, p 251–258, 2004) on the characteristics of nursing homes and their residents using 1999 NNHS data. It extends that study by separating nonmetropolitan counties into micropolitan and other nonmetro counties in order to understand differences in nursing home use across three county types—metro, micro and other nonmetro counties. A stepwise decline in the average number of beds per nursing home is discernible from metro counties to micro counties to other nonzero counties, which is associated with a similar drop across the three county types in the proportion of nursing homes with any specialty units. The survey results on residents in the 2004 NNHS confirms Duncan and Radcliff’s speculation that they are more likely to be admitted from acute-care hospitals if the nursing home is in a metro or micro county than if it is in other nonmetro counties. Finally, the chapter examines the 2004 wave of the Health and Retirement Survey to discern which risk factors for nursing-home residency discriminate among older adults in the three county types. Similar to von Reichert et al. (Chap. 15, this volume), although contrary to other earlier studies, I find that a higher percentage of nonmetro (than metro) older adults have relatives and friends living close by. Perhaps that is why nonmetro residence does not pose a greater risk than metro residence of living in a nursing home. Implications for policy and future research are explored.

Keywords

Nursing Home Group Quarter County Type Nonmetropolitan County Rural Elder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht. 2013

Authors and Affiliations

  1. 1.Department of SociologyMichigan State UniversityEast LansingUSA

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